I need to check for duplicate and near-duplicate rows across the tables. Let me start by examining the key tables for exact duplicates and then look for near-duplicates. ```json { "action": "tool_call", "reasoning": "Check for exact duplicate rows in the largest tables (beer_ratings, beers, place_ratings, users, places, brewers) by comparing total row count against distinct row combinations. I'll use a single query to check all tables efficiently.", "tool": "query_sql", "args": { "sql": "WITH duplicate_checks AS (SELECT 'beer_ratings' as table_name, COUNT(*) as total_rows, COUNT(DISTINCT (beer_id, user_id, created_at, rating_overall, rating_aroma, rating_appearance, rating_flavor, rating_mouthfeel, rating_palate)) as distinct_rows FROM raw.beer_ratings UNION ALL SELECT 'beers', COUNT(*), COUNT(DISTINCT (beer_id, name, brewer_id, style_id, abv, ibu, calories, created_at)) FROM raw.beers UNION ALL SELECT 'place_ratings', COUNT(*), COUNT(DISTINCT (place_id, user_id, created_at, rating_overall, rating_atmosphere, rating_service, rating_selection, rating_food)) FROM raw.place_ratings UNION ALL SELECT 'users', COUNT(*), COUNT(DISTINCT (user_id, user_name, created_at, beer_ratings_count, place_ratings_count)) FROM raw.users UNION ALL SELECT 'places', COUNT(*), COUNT(DISTINCT (place_id, name, place_type_id, city, state_id, country_id, latitude, longitude)) FROM raw.places UNION ALL SELECT 'brewers', COUNT(*), COUNT(DISTINCT (brewer_id, name, city, state_id, country_id, opened_at)) FROM raw.brewers) SELECT table_name, total_rows, distinct_rows, total_rows - distinct_rows as duplicate_count, ROUND(100.0 * (total_rows - distinct_rows) / total_rows, 2) as duplicate_pct FROM duplicate_checks WHERE total_rows > distinct_rows ORDER BY duplicate_count DESC", "purpose": "Identify exact duplicate rows across major tables by comparing total row count to distinct combinations of key columns" } } ```